{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logical and\n",
      "epoch0 sample0, [1 2 0 0 0 0 0]\n",
      "epoch0 sample1, [1 2 0 1 -1 0 -1]\n",
      "epoch0 sample2, [0 2 -1 1 0 -1 -1]\n",
      "epoch0 sample3, [0 1 -2 0 1 1 1]\n",
      "epoch1 sample0, [1 2 -1 0 0 0 0]\n",
      "epoch1 sample1, [1 2 -1 0 0 0 0]\n",
      "epoch1 sample2, [1 2 -1 1 0 -1 -1]\n",
      "epoch1 sample3, [1 1 -2 0 1 1 1]\n",
      "epoch2 sample0, [2 2 -1 0 0 0 0]\n",
      "epoch2 sample1, [2 2 -1 1 -1 0 -1]\n",
      "epoch2 sample2, [1 2 -2 0 0 0 0]\n",
      "epoch2 sample3, [1 2 -2 1 0 0 0]\n",
      "epoch3 sample0, [1 2 -2 0 0 0 0]\n",
      "epoch3 sample1, [1 2 -2 0 0 0 0]\n",
      "epoch3 sample2, [1 2 -2 0 0 0 0]\n",
      "epoch3 sample3, [1 2 -2 1 0 0 0]\n",
      "epoch4 sample0, [1 2 -2 0 0 0 0]\n",
      "epoch4 sample1, [1 2 -2 0 0 0 0]\n",
      "epoch4 sample2, [1 2 -2 0 0 0 0]\n",
      "epoch4 sample3, [1 2 -2 1 0 0 0]\n",
      "epoch5 sample0, [1 2 -2 0 0 0 0]\n",
      "epoch5 sample1, [1 2 -2 0 0 0 0]\n",
      "epoch5 sample2, [1 2 -2 0 0 0 0]\n",
      "epoch5 sample3, [1 2 -2 1 0 0 0]\n",
      "epoch6 sample0, [1 2 -2 0 0 0 0]\n",
      "epoch6 sample1, [1 2 -2 0 0 0 0]\n",
      "epoch6 sample2, [1 2 -2 0 0 0 0]\n",
      "epoch6 sample3, [1 2 -2 1 0 0 0]\n",
      "epoch7 sample0, [1 2 -2 0 0 0 0]\n",
      "epoch7 sample1, [1 2 -2 0 0 0 0]\n",
      "epoch7 sample2, [1 2 -2 0 0 0 0]\n",
      "epoch7 sample3, [1 2 -2 1 0 0 0]\n",
      "epoch8 sample0, [1 2 -2 0 0 0 0]\n",
      "epoch8 sample1, [1 2 -2 0 0 0 0]\n",
      "epoch8 sample2, [1 2 -2 0 0 0 0]\n",
      "epoch8 sample3, [1 2 -2 1 0 0 0]\n",
      "epoch9 sample0, [1 2 -2 0 0 0 0]\n",
      "epoch9 sample1, [1 2 -2 0 0 0 0]\n",
      "epoch9 sample2, [1 2 -2 0 0 0 0]\n",
      "epoch9 sample3, [1 2 -2 1 0 0 0]\n",
      "logical or\n",
      "epoch0 sample0, [1 2 0 0 0 0 0]\n",
      "epoch0 sample1, [1 2 0 1 0 0 0]\n",
      "epoch0 sample2, [1 2 0 1 0 0 0]\n",
      "epoch0 sample3, [1 2 0 1 0 0 0]\n",
      "epoch1 sample0, [1 2 0 0 0 0 0]\n",
      "epoch1 sample1, [1 2 0 1 0 0 0]\n",
      "epoch1 sample2, [1 2 0 1 0 0 0]\n",
      "epoch1 sample3, [1 2 0 1 0 0 0]\n",
      "epoch2 sample0, [1 2 0 0 0 0 0]\n",
      "epoch2 sample1, [1 2 0 1 0 0 0]\n",
      "epoch2 sample2, [1 2 0 1 0 0 0]\n",
      "epoch2 sample3, [1 2 0 1 0 0 0]\n",
      "epoch3 sample0, [1 2 0 0 0 0 0]\n",
      "epoch3 sample1, [1 2 0 1 0 0 0]\n",
      "epoch3 sample2, [1 2 0 1 0 0 0]\n",
      "epoch3 sample3, [1 2 0 1 0 0 0]\n",
      "epoch4 sample0, [1 2 0 0 0 0 0]\n",
      "epoch4 sample1, [1 2 0 1 0 0 0]\n",
      "epoch4 sample2, [1 2 0 1 0 0 0]\n",
      "epoch4 sample3, [1 2 0 1 0 0 0]\n",
      "epoch5 sample0, [1 2 0 0 0 0 0]\n",
      "epoch5 sample1, [1 2 0 1 0 0 0]\n",
      "epoch5 sample2, [1 2 0 1 0 0 0]\n",
      "epoch5 sample3, [1 2 0 1 0 0 0]\n",
      "epoch6 sample0, [1 2 0 0 0 0 0]\n",
      "epoch6 sample1, [1 2 0 1 0 0 0]\n",
      "epoch6 sample2, [1 2 0 1 0 0 0]\n",
      "epoch6 sample3, [1 2 0 1 0 0 0]\n",
      "epoch7 sample0, [1 2 0 0 0 0 0]\n",
      "epoch7 sample1, [1 2 0 1 0 0 0]\n",
      "epoch7 sample2, [1 2 0 1 0 0 0]\n",
      "epoch7 sample3, [1 2 0 1 0 0 0]\n",
      "epoch8 sample0, [1 2 0 0 0 0 0]\n",
      "epoch8 sample1, [1 2 0 1 0 0 0]\n",
      "epoch8 sample2, [1 2 0 1 0 0 0]\n",
      "epoch8 sample3, [1 2 0 1 0 0 0]\n",
      "epoch9 sample0, [1 2 0 0 0 0 0]\n",
      "epoch9 sample1, [1 2 0 1 0 0 0]\n",
      "epoch9 sample2, [1 2 0 1 0 0 0]\n",
      "epoch9 sample3, [1 2 0 1 0 0 0]\n",
      "logical xor\n",
      "epoch0 sample0, [1 2 0 0 0 0 0]\n",
      "epoch0 sample1, [1 2 0 1 0 0 0]\n",
      "epoch0 sample2, [1 2 0 1 0 0 0]\n",
      "epoch0 sample3, [1 2 0 1 -1 -1 -1]\n",
      "epoch1 sample0, [0 1 -1 0 0 0 0]\n",
      "epoch1 sample1, [0 1 -1 0 1 0 1]\n",
      "epoch1 sample2, [1 1 0 1 0 0 0]\n",
      "epoch1 sample3, [1 1 0 1 -1 -1 -1]\n",
      "epoch2 sample0, [0 0 -1 0 0 0 0]\n",
      "epoch2 sample1, [0 0 -1 0 1 0 1]\n",
      "epoch2 sample2, [1 0 0 0 0 1 1]\n",
      "epoch2 sample3, [1 1 1 1 -1 -1 -1]\n",
      "epoch3 sample0, [0 0 0 0 0 0 0]\n",
      "epoch3 sample1, [0 0 0 0 1 0 1]\n",
      "epoch3 sample2, [1 0 1 1 0 0 0]\n",
      "epoch3 sample3, [1 0 1 1 -1 -1 -1]\n",
      "epoch4 sample0, [0 -1 0 0 0 0 0]\n",
      "epoch4 sample1, [0 -1 0 0 1 0 1]\n",
      "epoch4 sample2, [1 -1 1 0 0 1 1]\n",
      "epoch4 sample3, [1 0 2 1 -1 -1 -1]\n",
      "epoch5 sample0, [0 -1 1 1 0 0 -1]\n",
      "epoch5 sample1, [0 -1 0 0 1 0 1]\n",
      "epoch5 sample2, [1 -1 1 0 0 1 1]\n",
      "epoch5 sample3, [1 0 2 1 -1 -1 -1]\n",
      "epoch6 sample0, [0 -1 1 1 0 0 -1]\n",
      "epoch6 sample1, [0 -1 0 0 1 0 1]\n",
      "epoch6 sample2, [1 -1 1 0 0 1 1]\n",
      "epoch6 sample3, [1 0 2 1 -1 -1 -1]\n",
      "epoch7 sample0, [0 -1 1 1 0 0 -1]\n",
      "epoch7 sample1, [0 -1 0 0 1 0 1]\n",
      "epoch7 sample2, [1 -1 1 0 0 1 1]\n",
      "epoch7 sample3, [1 0 2 1 -1 -1 -1]\n",
      "epoch8 sample0, [0 -1 1 1 0 0 -1]\n",
      "epoch8 sample1, [0 -1 0 0 1 0 1]\n",
      "epoch8 sample2, [1 -1 1 0 0 1 1]\n",
      "epoch8 sample3, [1 0 2 1 -1 -1 -1]\n",
      "epoch9 sample0, [0 -1 1 1 0 0 -1]\n",
      "epoch9 sample1, [0 -1 0 0 1 0 1]\n",
      "epoch9 sample2, [1 -1 1 0 0 1 1]\n",
      "epoch9 sample3, [1 0 2 1 -1 -1 -1]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "samples_and = [\n",
    "    [0,0,0],\n",
    "    [1,0,0],\n",
    "    [0,1,0],\n",
    "    [1,1,1],\n",
    "]\n",
    "samples_or = [\n",
    "    [0,0,0],\n",
    "    [1,0,1],\n",
    "    [0,1,1],\n",
    "    [1,1,1],\n",
    "]\n",
    "samples_xor = [\n",
    "    [0,0,0],\n",
    "    [1,0,1],\n",
    "    [0,1,1],\n",
    "    [1,1,0],\n",
    "]\n",
    "def perceptron(samples):\n",
    "    w = np.array([1,2])\n",
    "    b = 0\n",
    "    a = 1  #步长\n",
    "    for i in range(10):\n",
    "        for j in range(4):\n",
    "            x = np.array(samples[j][:2])\n",
    "            y = 1 if np.dot(w,x) + b >0 else 0\n",
    "            d = np.array(samples[j][2])\n",
    "            delta_b = a *(d-y)\n",
    "            delta_w = a*(d-y) *x\n",
    "            print('epoch{} sample{}, [{} {} {} {} {} {} {}]'.format(i,j,w[0],w[1],b,y,delta_w[0],delta_w[1],delta_b))\n",
    "            w = w +delta_w\n",
    "            b = b+ delta_b\n",
    "            \n",
    "\n",
    "if __name__ == '__main__':\n",
    "        print('logical and')\n",
    "        perceptron(samples_and)\n",
    "        print('logical or')\n",
    "        perceptron(samples_or)\n",
    "        print('logical xor')\n",
    "        perceptron(samples_xor)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure1 = plt.figure()\n",
    "figure2 = plt.figure()\n",
    "ax1 = Axes3D(figure1)\n",
    "ax2 = Axes3D(figure2)\n",
    "X = np.arange(0,2,1)\n",
    "Y = np.arange(0,2,1)\n",
    "X, Y = np.meshgrid(X,Y)\n",
    "Z1 = X + 2*Y -2\n",
    "ax1.plot_surface(X,Y,Z1)\n",
    "Z2 = X + 2*Y \n",
    "ax2.plot_surface(X,Y,Z2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  从上图可以看出 通过一个感知器构造出来的函数，在三维空间中是一个平面，为了保证其能正确从输入到输出，[1,0]点和[0,1]点的输出值必须在[0,0]点和[1,1]点之间。通过阶跃函数通过[1,0]点和[0,1]点 是否大于0 区分与和或问题。但是在异或问题中，[1,0]点和[0,1]点 输出值大于[0,0]点和[1,1]，因此无法通过感知器来进行区分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
